Methods and Apparatus to Provide Machine Assisted Programming

2. The apparatus of claim 1, wherein the first machine learning model is to identify the cluster based on a similarity between the first feature vector and the stored feature vectors of the cluster.

3. The apparatus of claim 1, wherein the first machine learning model is to generate a new cluster for the first feature vector when there is no clusters that are similar to the first feature vector.

4. The apparatus of claim 1, wherein the first machine learning model is trained based on at least one of local libraries, external libraries, internal code, or new code that does not match a cluster.

5. The apparatus of claim 1, wherein the second machine learning model is to compare efficiency of parameters for algorithms corresponding to stored feature vectors within the cluster.

6. The apparatus of claim 5, wherein the second machine learning model is to determine the efficiency of the parameters for algorithms corresponding to the stored feature vectors based on documentation corresponding to the algorithms.

7. The apparatus of claim 5, further including a function executor to execute a third algorithm corresponding to a stored feature, the second machine learning model to determine the efficiency of the parameter for the algorithm based on the execution of the third algorithm.

8. The apparatus of claim 1, wherein the parameter is selected by a user.

9. The apparatus of claim 1, wherein the second machine learning model is determined based on context information including at least one of availability of resources or purpose of the compiled code.

10. The apparatus of claim 1, wherein the recommendation is displayed to a user.

11. The apparatus of claim 1, wherein the second machine learning model is to update recommendations based on user feedback to the recommendation.

13. The computer readable storage medium of claim 12, wherein the instructions cause the machine to identify the cluster based on a similarity between the first feature vector and the stored feature vectors of the cluster.

14. The computer readable storage medium of claim 12, wherein the instructions cause the machine to generate a new cluster for the first feature vector when there is no clusters that are similar to the first feature vector.

15. The computer readable storage medium of claim 12, wherein the instructions cause the machine to train a first machine learning model based on at least one of local libraries, external libraries, internal code, or new code that does not match a cluster.

16. The computer readable storage medium of claim 12, wherein the instructions cause the machine to compare efficiency of parameters for algorithms corresponding to stored feature vectors within the cluster.

17. The computer readable storage medium of claim 16, wherein the instructions cause the machine to determine the efficiency of the parameters for algorithms corresponding to the stored feature vectors based on documentation corresponding to the algorithms.

19. The computer readable storage medium of claim 12, wherein the parameter is selected by a user.

20. The computer readable storage medium of claim 12, wherein the instructions cause the machine to determine a machine learning model based on context information including at least one of availability of resources or purpose of the compiled code.

21. The computer readable storage medium of claim 12, wherein the recommendation is displayed to a user.

22. The computer readable storage medium of claim 12, wherein the instructions cause the machine to update recommendations based on user feedback to the recommendation.

24. The method of claim 23, further including identifying the cluster based on a similarity between the first feature vector and the stored feature vectors of the cluster.

25. The method of claim 23, further including generating a new cluster for the first feature vector when there is no clusters that are similar to the first feature vector.